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    "# [目标检测7日打卡营](https://aistudio.baidu.com/aistudio/course/introduce/1617?directly=1&shared=1)作业二：RCNN系列模型实战\n",
    "\n",
    "## 实战数据集及框架\n",
    "\n",
    "- 印刷电路板（PCB）瑕疵数据集：[数据下载链接](http://robotics.pkusz.edu.cn/resources/dataset/)，是一个公共的合成PCB数据集，由北京大学发布，其中包含1386张图像以及6种缺陷（缺失孔，鼠标咬伤，开路，短路，杂散，伪铜），用于检测，分类和配准任务。我们选取了其中适用与检测任务的693张图像，随机选择593张图像作为训练集，100张图像作为验证集。\n",
    "- PaddleDetection：飞桨推出的PaddleDetection是端到端目标检测开发套件，旨在帮助开发者更快更好地完成检测模型的训练、精度速度优化到部署全流程。该框架中提供了丰富的数据增强、网络组件、损失函数等模块，集成了模型压缩和跨平台高性能部署能力。目前基于PaddleDetection已经完成落地的项目涉及工业质检、遥感图像检测、无人巡检等多个领域。\n",
    "\n",
    "## 作业描述\n",
    "基于PaddleDetection中的RCNN系列算法，完成印刷电路板（PCB）瑕疵数据集的训练与评估任务，在验证集上采用IoU=0.5，area=all的mAP作为评价指标，得分=mAP * 100，范围[0,100]，分数越高模型效果越好，及格线是60分。鼓励大家使用GPU训练，CPU训练的结果不纳入排行榜。\n",
    "\n",
    "提示：\n",
    "1. 增大训练轮数有助于网络收敛的更好，可提高mAP；\n",
    "2. 增加FPN、更换Backbone等组件可增强网络的表达能力；\n",
    "2. 在PaddleDetection中configs中有不同种类的RCNN系列模型，可以寻找RCNN系列性能更强的模型配置文件。（注意：由于数据集无物体分割mask信息，所以不支持Mask-RCNN）\n",
    "\n",
    "## 解决方案：Cascade R-CNN迁移学习\n",
    "**论文名称:**Cascade** **R**\\-**CNN**: Delving into High Quality Object Detection**\n",
    "\n",
    "**作者：Zhaowei Cai & Nuno Vasconcelos**\n",
    "\n",
    "[论文链接：https://arxiv.org/abs/1712.00726](https://arxiv.org/abs/1712.00726)\n",
    "\n",
    "[代码链接：https://github.com/zhaoweicai/**cascade**\\-rcnn](https://github.com/zhaoweicai/cascade-rcnn)\n",
    "### Cascade R-CNN原理\n",
    "**CascadeRCNN这篇论文主要解决了在用FasterRCNN进行目标检测中，检测框不是特别准，容易出现噪声干扰的问题，即close false positive**。\n",
    "\n",
    "首先作者分析了为什么会有这个问题，通过实验发现，因为在基于anchor的检测方法中，我们一般会设置训练的正负样本（用于训练分类以及对正样本进行坐标回归），选取正负样本的方式主要利用候选框与ground truth的IOU占比，常用的比例是50%，即IOU>0.5的作为正样本，IOU<0.3作为负样本等，但是这样就带来了一个问题，**阈值取0.5是最好的吗**？\n",
    "![file](https://ai-studio-static-online.cdn.bcebos.com/ed01e087539f4e4095222d309df9d8e719cb39d031a242c4ad4f409f7954b6a0)\n",
    "\n",
    "作者通过实验发现：\n",
    "\n",
    "**1. 设置不同阈值，阈值越高，其网络对准确度较高的候选框的作用效果越好。**\n",
    "\n",
    "**2. 不论阈值设置多少，训练后的网络对输入的proposal都有一定的优化作用。**\n",
    "\n",
    "基于这两点，作者设计了**Cascade** **R**\\-**CNN**网络，即通过级联的**R**\\-**CNN**网络，每个级联的**R**\\-**CNN**设置不同的IOU阈值，这样每个网络输出的准确度提升一点，用作下一个更高精度的网络的输入，逐步将网络输出的准确度进一步提高。\n",
    "\n",
    "**一句话总结就是：**Cascade** **R**\\-**CNN**就是使用不同的IOU阈值，训练了多个级联的检测器。**\n",
    "### 学习笔记\n",
    "首先就是如何在老师的指导下看懂左边的实验结果图:\n",
    "\n",
    "![file](https://ai-studio-static-online.cdn.bcebos.com/2320196e9cd44ed092253305b65b9b831de58b0afee74dda9d79711ab8d3dd63)\n",
    "\n",
    "<font size=5>横轴是第一阶段RPN生成的RoI和真实框的IoU，这个IoU要输入给第二阶段的BBox Head。</font>\n",
    "\n",
    "![file](https://ai-studio-static-online.cdn.bcebos.com/1df9bf1782524f3c93a7687a1672e9507a8ab857dc4b4ed3bda0a146d6187949)\n",
    "\n",
    "<font size=5>纵轴是经过BBox Head后得到的新bbox和真实框的IoU，显然，目标检测模型最终的bbox效果，看的是纵轴的效果。</font>\n",
    "\n",
    "在左图中，根据第一阶段不同IoU阈值设定在第二阶段的Output IoU表现，大致可以分成三个部分：\n",
    "- 标记为①的部分，IoU在0.55~0.6之间时，基于0.5的IoU阈值训练的回归器输出最佳（蓝色线）\n",
    "- 标记为②的部分，IoU在0.6~0.75之间，基于0.6的IoU阈值训练的回归器输出最佳（绿色线）\n",
    "- 标记为③的部分，IoU在0.75以上，基于0.7的阈值训练出的回归器输出最佳（红色线））\n",
    "\n",
    "<font size=5 color=red>这个实验结果充分表明，第一阶段的IoU阈值并不是越大越好，因为基于0.7的IoU阈值训练出的检测器中正样本过少，因此正样本的多样性不够，容易导致训练的过拟合，因此在验证集上表现不佳。</font>\n",
    "<font size=5>进而得到右图的结论，单一阈值训练出的检测器效果非常有限。</font>\n",
    "\n",
    "<font size=5 color=green>接着，论文作者尝试了不同方式的Faster R-CNN网络结构的改进实验。最终，发现Cascade R-CNN的网络结构效果最好。</font>\n",
    "\n",
    "![file](https://ai-studio-static-online.cdn.bcebos.com/ab4d44f23b4c42f383f7e81d493c93685bf88e39809e40f68a78edb0ae3bf418)\n",
    "\n",
    "<font size=5 color=red>具体就是增加了一个级联的步骤，BBox Head输出的偏移量，会应用到输入的RoI上，修正得到新的RoI，进入到下一个阶段，做正负样本的选择、RoI Align的特征抽取，总共对RPN输出的ROI做了三次微调。</font>\n",
    "\n",
    "<font size=5>Cascade R-CNN是在训练的时候就进行重新采样，训练的时候不同的stage的输入数据分布已经是不同了，可以避免出现过拟合的问题。</font>\n",
    "\n",
    "更多参考资料：\n",
    "- [一文带你读懂Cascade R-CNN，一个使你的检测更加准确的网络](https://blog.csdn.net/Chunfengyanyulove/article/details/86414810)\n",
    "- [Cascade R-CNN：向高精度目标检测器迈进](https://zhuanlan.zhihu.com/p/40207812)\n",
    "- [Cascade R-CNN 详细解读](https://zhuanlan.zhihu.com/p/42553957)\n",
    "### 迁移学习思路\n",
    "PaddleDetecion上其实提供了多种Cascade R-CNN的配置文件和预训练模型，本文参考了年初AI识虫比赛[aaaLKgo](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/110992)大佬的思路：\n",
    "> 一定要使用预训练参数，object365是目前比较好的大规模图像数据集，其预训练的参数效果都比较好，即使没有预训练参数，也要自己去训练一下，再作为预训练参数拿过来；\n",
    "因此选择的网络结构如下：\n",
    "\n",
    "<font size=5>Deformable 卷积网络v2</font>\n",
    "\n",
    "| 骨架网络 | 网络类型 | 卷积 | 每张GPU图片个数 | 学习率策略 | 推理时间(fps) | Box AP | Mask AP | 下载 | 配置文件 |\n",
    "| :-- | :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |\n",
    "| SENet154-vd-FPN | Cascade Mask | c3-c5 | 1 | 1.44x | \\- | 51.9 | 43.9 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_mask_rcnn_dcnv2_se154_vd_fpn_gn_s1x.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/dcn/cascade_mask_rcnn_dcnv2_se154_vd_fpn_gn_s1x.yml) |\n",
    "\n",
    "在原配置文件基础上，除调正学习率外未作更多修改，在20000个iters内mAP就可以很快提升到98以上。配置文件与注释已在下文给出。\n",
    "## 开始训练\n",
    "### 数据准备\n",
    "首先将印刷电路板（PCB）瑕疵数据集与PaddleDetection代码解压到`~/work/`目录中："
   ]
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   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "# 解压数据集\n",
    "!tar -xf data/data52914/PCB_DATASET.tar -C ~/work/\n",
    "# 解压PaddleDetection源码\n",
    "!tar -xf data/data52899/PaddleDetection.tar -C ~/work/"
   ]
  },
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   },
   "source": [
    "### 环境安装\n",
    "进行训练前需要安装PaddleDetection所需的依赖包，执行以下命令即可安装："
   ]
  },
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   "execution_count": 2,
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      "Requirement already satisfied: numpy>=1.7.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib>=2.1.0->pycocotools) (1.16.4)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib>=2.1.0->pycocotools) (0.10.0)\n",
      "Requirement already satisfied: pytz in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib>=2.1.0->pycocotools) (2019.3)\n",
      "Requirement already satisfied: six>=1.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib>=2.1.0->pycocotools) (1.15.0)\n",
      "Building wheels for collected packages: pycocotools\n",
      "  Building wheel for pycocotools (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for pycocotools: filename=pycocotools-2.0.2-cp37-cp37m-linux_x86_64.whl size=278365 sha256=096b6568930812fa72fc2b9db5432ad8010d0809c97791d7c720f7f04cc81eb2\n",
      "  Stored in directory: /home/aistudio/.cache/pip/wheels/fb/44/67/8baa69040569b1edbd7776ec6f82c387663e724908aaa60963\n",
      "Successfully built pycocotools\n",
      "Installing collected packages: pycocotools\n",
      "Successfully installed pycocotools-2.0.2\n"
     ]
    }
   ],
   "source": [
    "%cd ~/work/PaddleDetection\n",
    "! pip install -r requirements.txt\n",
    "! pip install pycocotools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 修改配置文件\n",
    "PaddleDetection中提供的配置文件是在8卡GPU环境下的配置，我们需要调整配置文件：\n",
    "包括最大训练轮数(max_iters)，类别数(num_classes)，学习率(LearningRate)相关参数，数据Reader中TrainReader与EvalReader数据集路径等参数。\n",
    "\n",
    "**注意：如在CPU下完成本作业，数据集将缩减到很小，训练集50张，测试集10张，但不推荐使用CPU训练**，GPU下请选择`train.json`和`val.json`标注文件，CPU下请选择`train_cpu.json`和`val_cpu.json`标注文件。\n",
    "\n",
    "## GPU下配置\n",
    "提示：\n",
    "- 为保证模型正常训练不出Nan，学习率要根据GPU卡数，batch size变换而做线性变换，比如这里我们将GPU卡数8->1，所以base_lr除以8即可；\n",
    "- 训练轮数与Epoch转换关系：根据训练集数量与总batch_size大小计算epoch数，然后将epoch数换算得到训练总轮数max_iters。milestones（学习率变化界限）也是同理。配置文件中batch_size=2，训练集数量为593，训练6个Epoch，在单卡GPU上训练，max_iters=593x6=3558。同理计算milestones为: [2372, 3261]\n",
    "- RCNN系列模型的类别数num_classes需要加上背景background，所以num_classes=6+1=7\n",
    "\n",
    "### configs/obj365/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas.yml配置文件：\n",
    "```YAML\n",
    "#####################################基础配置#####################################\n",
    "\n",
    "# 检测模型的名称\n",
    "architecture: CascadeRCNN\n",
    "# 最大迭代次数，而一个iter会运行batch_size * device_num张图片\n",
    "max_iters: 50000\n",
    "# 模型保存间隔，如果训练时eval设置为True，会在保存后进行验证\n",
    "snapshot_iter: 2000\n",
    "# 默认使用GPU运行，设为False时使用CPU运行\n",
    "use_gpu: true\n",
    "# 默认打印log的间隔，默认200\n",
    "log_iter: 200\n",
    "# 输出指定区间的平均结果，默认200，即输出20次的平均结果\n",
    "log_smooth_window: 200\n",
    "# 训练权重的保存路径\n",
    "save_dir: output\n",
    "# 模型的预训练权重，默认是从指定url下载\n",
    "pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/cascade_mask_rcnn_dcnv2_se154_vd_fpn_gn_coco_pretrained.tar\n",
    "# 用于模型验证或测试的训练好的权重\n",
    "weights: output/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas/model_final\n",
    "# 验证模型的评测标准，可以选择COCO或者VOC\n",
    "metric: COCO\n",
    "# 用于训练或验证的数据集的类别数目\n",
    "# **其中包含背景类，即7=6 + 1（背景类）**\n",
    "num_classes: 7\n",
    "\n",
    "# CascadeRCNN元结构，包括了以下主要组件\n",
    "CascadeRCNN:\n",
    "  backbone: SENet\n",
    "  fpn: FPN\n",
    "  rpn_head: FPNRPNHead\n",
    "  roi_extractor: FPNRoIAlign\n",
    "  bbox_head: CascadeBBoxHead\n",
    "  bbox_assigner: CascadeBBoxAssigner\n",
    "\n",
    "# 主干网络\n",
    "SENet:\n",
    "  # SENet深度，默认152\n",
    "  depth: 152\n",
    "  # 主干网络返回的主要阶段特征用于FPN作进一步的特征融合\n",
    "  # 默认从[2,3,4,5]返回特征\n",
    "  feature_maps: [2, 3, 4, 5]\n",
    "  # 是否在训练中固定某些权重，默认从第2阶段开始固定，即SENet的stage 1\n",
    "  freeze_at: 2\n",
    "  group_width: 4\n",
    "  groups: 64\n",
    "  norm_type: bn\n",
    "  # 是否停止norm layer的梯度回传，默认是\n",
    "  freeze_norm: True\n",
    "  # SENet模型的类型,  默认使用'd'类型\n",
    "  variant: d\n",
    "  dcn_v2_stages: [3, 4, 5]\n",
    "  std_senet: True\n",
    "\n",
    "# FPN多特征融合\n",
    "FPN:\n",
    "  # FPN使用主干网络最低层特征，默认是resnet第2阶段的输出\n",
    "  min_level: 2\n",
    "  # FPN使用主干网络最高层特征，默认是resnet第5阶段后添加额外卷积操作变<成了FPN的第6个，总共有5个阶段\n",
    "  max_level: 6\n",
    "  # FPN输出特征的通道数量, 默认是256\n",
    "  num_chan: 256\n",
    "  # 特征图缩放比例, 默认是[0.03125, 0.0625, 0.125, 0.25]\n",
    "  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]\n",
    "  freeze_norm: False\n",
    "  norm_type: gn\n",
    "\n",
    "# 检测第一阶段RPN\n",
    "FPNRPNHead:\n",
    "  # 根据特征图尺寸，在特征图的每个位置生成N个大小、长宽比各不同anchor\n",
    "  # N = anchor_sizes * aspect_ratios\n",
    "  # 具体实现参考[API](fluid.layers.anchor_generator)\n",
    "  anchor_generator:\n",
    "    anchor_sizes: [32, 64, 128, 256, 512]\n",
    "    aspect_ratios: [0.5, 1.0, 2.0]\n",
    "    stride: [16.0, 16.0]\n",
    "    variance: [1.0, 1.0, 1.0, 1.0]\n",
    "  anchor_start_size: 32\n",
    "  min_level: 2\n",
    "  max_level: 6\n",
    "  num_chan: 256\n",
    "  # 首先计算Anchor和GT BBox之间的IoU，为每个Anchor匹配上GT，\n",
    "  # 然后根据阈值过滤掉IoU低的Anchor，得到最终的Anchor及其GT进行loss计算\n",
    "  # 具体实现参考[API](fluid.layers.rpn_target_assign)\n",
    "  rpn_target_assign:\n",
    "    rpn_batch_size_per_im: 256\n",
    "    rpn_fg_fraction: 0.5\n",
    "    rpn_positive_overlap: 0.7\n",
    "    rpn_negative_overlap: 0.3\n",
    "    rpn_straddle_thresh: 0.0\n",
    "  # 首先取topk个分类分数高的anchor\n",
    "  # 然后通过NMS对这topk个anchor进行重叠度检测，对重叠高的两个anchor只保留得分高的\n",
    "  # 训练和测试阶段主要区别在最后NMS保留的Anchor数目\n",
    "  # 训练时输出2000个proposals，推理时输出1000个proposals\n",
    "  # 具体实现参考[API](fluid.layers.generate_proposals)\n",
    "  train_proposal:\n",
    "    min_size: 0.0\n",
    "    nms_thresh: 0.7\n",
    "    pre_nms_top_n: 2000\n",
    "    post_nms_top_n: 2000\n",
    "  test_proposal:\n",
    "    min_size: 0.0\n",
    "    nms_thresh: 0.7\n",
    "    pre_nms_top_n: 1000\n",
    "    post_nms_top_n: 1000\n",
    "\n",
    "# 对FPN每层执行RoIAlign后，然后合并输出结果，用于BBox Head计算\n",
    "FPNRoIAlign:\n",
    "  # 用于抽取特征特征的FPN的层数，默认为4\n",
    "  canconical_level: 4\n",
    "  # 用于抽取特征特征的FPN的特征图尺寸，默认为224\n",
    "  canonical_size: 224\n",
    "  # 用于抽取特征特征的最底层FPN，默认是2\n",
    "  min_level: 2\n",
    "  # 用于抽取特征特征的最高层FPN，默认是5\n",
    "  max_level: 5\n",
    "  # 输出bbox的特征图尺寸，默认为7\n",
    "  box_resolution: 7\n",
    "  # roi extractor的采样率，默认为2\n",
    "  sampling_ratio: 2\n",
    "\n",
    "# 求rpn生成的roi跟gt bbox之间的iou，然后根据阈值进行过滤，保留一定数量的roi\n",
    "# 再根据gt bbox的标签，对roi进行标签赋值，即得到每个roi的类别\n",
    "# 具体实现参考[API](fluid.layers.generate_proposal_labels)\n",
    "# CascadeBBoxAssigner这里是级联的\n",
    "CascadeBBoxAssigner:\n",
    "  batch_size_per_im: 1024\n",
    "  bbox_reg_weights: [10, 20, 30]\n",
    "  bg_thresh_lo: [0.0, 0.0, 0.0]\n",
    "  bg_thresh_hi: [0.5, 0.6, 0.7]\n",
    "  fg_thresh: [0.5, 0.6, 0.7]\n",
    "  fg_fraction: 0.25\n",
    "\n",
    "# 输出检测框的Head\n",
    "CascadeBBoxHead:\n",
    "  head: CascadeXConvNormHead\n",
    "  # 通过NMS进行bbox过滤\n",
    "  # 具体实现参考[API](fluid.layers.multiclass_nms)\n",
    "  nms:\n",
    "    keep_top_k: 100\n",
    "    nms_threshold: 0.5\n",
    "    score_threshold: 0.05\n",
    "\n",
    "CascadeXConvNormHead:\n",
    "  norm_type: gn\n",
    "\n",
    "CascadeTwoFCHead:\n",
    "  mlp_dim: 1024\n",
    "\n",
    "#####################################训练配置#####################################\n",
    "\n",
    "# 学习率配置\n",
    "LearningRate:\n",
    "  # 初始学习率, 一般情况下8卡gpu，batch size为2时设置为0.02\n",
    "  # 可以根据具体情况，按比例调整\n",
    "  # 比如说4卡V100，bs=2时，设置为0.01\n",
    "  base_lr: 0.00125\n",
    "  # 学习率规划器\n",
    "  # 具体实现参考[API](fluid.layers.piecewise_decay)\n",
    "  schedulers:\n",
    "    # 学习率衰减策略\n",
    "    # 对于coco数据集，1个epoch大概需要7000个iter\n",
    "    # if step < 120000:\n",
    "    #    learning_rate = 0.1\n",
    "    # elif 120000 <= step < 160000:\n",
    "    #    learning_rate = 0.1 * 0.1\n",
    "    # else:\n",
    "    #    learning_rate = 0.1 * (0.1)**2\n",
    "  - !PiecewiseDecay\n",
    "    gamma: 0.1\n",
    "    milestones: [40000, 46000]\n",
    "    # 在训练开始时，调低学习率为base_lr * start_factor，然后逐步增长到base_lr，这个过程叫学习率热身，按照以下公式更新学习率\n",
    "    # linear_step = end_lr - start_lr\n",
    "    # lr = start_lr + linear_step * (global_step / warmup_steps)\n",
    "    # 具体实现参考[API](fluid.layers.linear_lr_warmup)\n",
    "  - !LinearWarmup\n",
    "    start_factor: 0.01\n",
    "    steps: 2000\n",
    "\n",
    "OptimizerBuilder:\n",
    "  # 默认使用SGD+Momentum进行训练\n",
    "  # 具体实现参考[API](fluid.optimizer)\n",
    "  optimizer:\n",
    "    momentum: 0.9\n",
    "    type: Momentum\n",
    "  # 默认使用L2权重衰减正则化\n",
    "  # 具体实现参考[API](fluid.regularizer)\n",
    "  regularizer:\n",
    "    factor: 0.0001\n",
    "    type: L2\n",
    "\n",
    "#####################################数据配置#####################################\n",
    "\n",
    "# 模型训练集设置参考\n",
    "# 训练、验证、测试使用的数据配置主要区别在数据路径、模型输入、数据增强参数设置\n",
    "TrainReader:\n",
    "  inputs_def:\n",
    "    fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']\n",
    "  dataset:\n",
    "    !COCODataSet\n",
    "    image_dir: images\n",
    "    anno_path: Annotations/train.json\n",
    "    dataset_dir: /home/aistudio/work/PCB_DATASET\n",
    "  sample_transforms:\n",
    "  - !DecodeImage\n",
    "    to_rgb: True\n",
    "  - !RandomFlipImage\n",
    "    prob: 0.5\n",
    "  - !NormalizeImage\n",
    "    is_channel_first: false\n",
    "    is_scale: False\n",
    "    mean:\n",
    "    - 102.9801\n",
    "    - 115.9465\n",
    "    - 122.7717\n",
    "    std:\n",
    "    - 1.0\n",
    "    - 1.0\n",
    "    - 1.0\n",
    "  - !ResizeImage\n",
    "    interp: 1\n",
    "    target_size:\n",
    "    - 608\n",
    "    - 640\n",
    "    - 800\n",
    "    - 1408\n",
    "    max_size: 1600\n",
    "    use_cv2: true\n",
    "  - !Permute\n",
    "    channel_first: true\n",
    "    to_bgr: false\n",
    "  batch_transforms:\n",
    "  - !PadBatch\n",
    "    pad_to_stride: 32\n",
    "  batch_size: 1\n",
    "  worker_num: 4\n",
    "  shuffle: true\n",
    "  class_aware_sampling: true\n",
    "  use_process: false\n",
    "\n",
    "EvalReader:\n",
    "  inputs_def:\n",
    "    fields: ['image', 'im_info', 'im_id', 'im_shape']\n",
    "  dataset:\n",
    "    !COCODataSet\n",
    "    image_dir: images\n",
    "    anno_path: Annotations/val.json\n",
    "    dataset_dir: /home/aistudio/work/PCB_DATASET\n",
    "  sample_transforms:\n",
    "  - !DecodeImage\n",
    "    to_rgb: True\n",
    "  - !NormalizeImage\n",
    "    is_channel_first: false\n",
    "    is_scale: False\n",
    "    mean:\n",
    "    - 102.9801\n",
    "    - 115.9465\n",
    "    - 122.7717\n",
    "    std:\n",
    "    - 1.0\n",
    "    - 1.0\n",
    "    - 1.0\n",
    "  - !ResizeImage\n",
    "    target_size: 800\n",
    "    max_size: 1333\n",
    "    interp: 1\n",
    "  - !Permute\n",
    "    channel_first: true\n",
    "    to_bgr: false\n",
    "  batch_transforms:\n",
    "  - !PadBatch\n",
    "    pad_to_stride: 32\n",
    "  batch_size: 1\n",
    "  drop_empty: false\n",
    "  worker_num: 2\n",
    "\n",
    "TestReader:\n",
    "  batch_size: 1\n",
    "  inputs_def:\n",
    "    fields: ['image', 'im_info', 'im_id', 'im_shape']\n",
    "  dataset:\n",
    "    !ImageFolder\n",
    "    anno_path: /home/aistudio/work/PCB_DATASET/Annotations/val.json\n",
    "  sample_transforms:\n",
    "  - !DecodeImage\n",
    "    to_rgb: True\n",
    "  - !NormalizeImage\n",
    "    is_channel_first: false\n",
    "    is_scale: False\n",
    "    mean:\n",
    "    - 102.9801\n",
    "    - 115.9465\n",
    "    - 122.7717\n",
    "    std:\n",
    "    - 1.0\n",
    "    - 1.0\n",
    "    - 1.0\n",
    "  - !Permute\n",
    "    channel_first: true\n",
    "    to_bgr: false\n",
    "  batch_transforms:\n",
    "  - !PadBatch\n",
    "    pad_to_stride: 32\n",
    "  worker_num: 2\n",
    "```\n",
    "\n",
    "### 配置文件参考\n",
    "加FPN组件的完整的配置文件，请在[https://github.com/PaddlePaddle/PaddleDetection/tree/release/0.4/configs/pcb](https://github.com/PaddlePaddle/PaddleDetection/tree/release/0.4/configs/pcb) 中查看或下载。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "! python -u tools/train.py -c configs/obj365/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas.yml --eval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 评估与预测\n",
    "如果在训练中加了`--eval`参数，在模型训练完就可得到mAP指标，如果要对模型单独计算mAP，可以运行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-23 16:46:32,627-WARNING: paddle.fluid.layers.matrix_nms OP not found, maybe a newer version of paddle is required.\n",
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "2020-09-23 16:46:36,277-INFO: places would be ommited when DataLoader is not iterable\n",
      "W0923 16:46:36.534276  3046 device_context.cc:252] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 9.2, Runtime API Version: 9.0\n",
      "W0923 16:46:36.538686  3046 device_context.cc:260] device: 0, cuDNN Version: 7.6.\n",
      "2020-09-23 16:46:46,412-INFO: Test iter 0\n",
      "2020-09-23 16:47:19,194-INFO: Test finish iter 100\n",
      "2020-09-23 16:47:19,194-INFO: Total number of images: 100, inference time: 2.9431218578714953 fps.\n",
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "2020-09-23 16:47:19,205-INFO: Start evaluate...\n",
      "Loading and preparing results...\n",
      "DONE (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.11s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.03s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.551\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.988\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.564\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.492\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.554\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.536\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.149\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.618\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.618\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.625\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.621\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.563\n"
     ]
    }
   ],
   "source": [
    " ! python -u tools/eval.py -c configs/obj365/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas.yml \\\n",
    "                -o weights=output/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas/26000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "对印刷电路板（PCB）瑕疵数据集个别图片进行可视化预测，可以运行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-23 16:49:29,022-WARNING: paddle.fluid.layers.matrix_nms OP not found, maybe a newer version of paddle is required.\n",
      "W0923 16:49:32.727718  3199 device_context.cc:252] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 9.2, Runtime API Version: 9.0\n",
      "W0923 16:49:32.732332  3199 device_context.cc:260] device: 0, cuDNN Version: 7.6.\n",
      "2020-09-23 16:49:42,485-INFO: Load categories from /home/aistudio/work/PCB_DATASET/Annotations/val.json\n",
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "2020-09-23 16:49:46,575-INFO: Infer iter 0\n",
      "2020-09-23 16:49:46,705-INFO: Detection bbox results save in output/04_missing_hole_10.jpg\n"
     ]
    }
   ],
   "source": [
    "! python -u tools/infer.py -c configs/obj365/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas.yml \\\n",
    "                --infer_img=../PCB_DATASET/images/04_missing_hole_10.jpg \\\n",
    "                -o weights=output/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas/26000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\r\n",
    "import matplotlib.pyplot as plt # plt 用于显示图片\r\n",
    "import numpy as np\r\n",
    "import cv2\r\n",
    "\r\n",
    "# 读取原始图片\r\n",
    "origin_pic = cv2.imread('output/04_missing_hole_10.jpg')\r\n",
    "origin_pic = cv2.cvtColor(origin_pic, cv2.COLOR_BGR2RGB)\r\n",
    "plt.imshow(origin_pic)\r\n",
    "plt.axis('off') # 不显示坐标轴\r\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PaddlePaddle 1.8.4 (Python 3.5)",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
